Cervical Cancer Diagnosis Model Using Extreme Gradient Boosting and Bioinspired Firefly Optimization

Khan, Irfan Ullah; Aslam, Nida; Alshehri, Rawan; Alzahrani, Seham; Ghamdi, Manal Al; Almalki, Atheer; Balabeed, Maryam

doi:10.1155/2021/5540024

Cited by 13 publications

(15 citation statements)

References 21 publications

(14 reference statements)

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“…IU Khan et al use ensemble-based classification techniques for disease classification. Random Forest, Extreme Gradient Boosting, and AdaBoost [81] are used for training process. Random forest improves the performance by adding many trees to reduce the overfitting.…”

Section: Traditional Machine Learning Algorithmsmentioning

confidence: 99%

“…XGBoost outperforms other models as it handles the sparse data and implements several optimization and regularization technique. [81] W Chen et al propose a "transfer learning based snapshot ensemble" (TLSE) method by integrating snapshot ensemble learning [82] with transfer learning. This method is evaluated on Herlev dataset [13] and achieves good accuracy.…”

Section: Traditional Machine Learning Algorithmsmentioning

confidence: 99%

“…Calculation Formula Precision [72][73][74][75][76][77][78][79][80][81][82][83] TP/(TP + FP) [72][73][74][75][76][77][78][79][80][81][82][83] Recall (Sensitivity) [72][73][74][75][76][77][78][79][80][81][82][83] TP/(TP + FN) [72][73][74][75][76][77][78][79][80][81][82][83] Specificity [72][73][74][75]<...>…”

Section: Table 3 Evaluation Metrics For the Classification Model Perf...mentioning

confidence: 99%

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Automated Detection and Classification of Cervical Cancer Using Pap Smear Microscopic Images: A Comprehensive Review and Future Perspectives

Shanthi¹,

Hareesha²,

Kudva³

2022

Eng. Sci.

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Computer aided categorization of smear images has been considered challenging in the past few decades. Cervical cancer is a main cause of mortality among women worldwide and more prevalent in underdeveloped countries. This disease can be successfully treated, even fully cured, if detected at its early phase. Computerized image analysis methods are primarily of great interest as they provide significant benefits for clinicians with reliable and timely diagnosis of the samples. Dedicated image analysis algorithms provide mathematical description of the region of interest which provide a great support to pathologists for decision making. In this review, we have outlined state of the art techniques expressed in prominent publications on computer assisted diagnostic system for cancer detection. By utilizing the domain aspects of cervical cancer, suitable methods and techniques are explored and presented. This review also presents a knowledge to assess the methodology used in the literature and emphasized some of the inadequacies and weaknesses in the reviewed methods. The study accentuates the future directions pertinent to the development of cost-effective, automated disease classification system that should be a significant advantage for countries with limited resources and treatment services.

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Section: Traditional Machine Learning Algorithmsmentioning

confidence: 99%

Section: Traditional Machine Learning Algorithmsmentioning

confidence: 99%

Section: Table 3 Evaluation Metrics For the Classification Model Perf...mentioning

confidence: 99%

See 1 more Smart Citation

Automated Detection and Classification of Cervical Cancer Using Pap Smear Microscopic Images: A Comprehensive Review and Future Perspectives

Shanthi¹,

Hareesha²,

Kudva³

2022

Eng. Sci.

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“…A significant component of machine learning lies with the distribution of data. An imbalanced dataset [23] indicates that some classes have a higher number of instances than others. As shown in Figure 2, the dataset includes an imbalanced set of data (63 versus 102).…”

Section: Random Oversampling/undersamplingmentioning

confidence: 99%

A Novel Method for Survival Prediction of Hepatocellular Carcinoma Using Feature-Selection Techniques

Ali

Orban

Ramasamy³

et al. 2022

Applied Sciences

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The World Health Organization (WHO) predicted that 10 million people would have died of cancer by 2020. According to recent studies, liver cancer is the most prevalent cancer worldwide. Hepatocellular carcinoma (HCC) is the leading cause of early-stage liver cancer. However, HCC occurs most frequently in patients with chronic liver conditions (such as cirrhosis). Therefore, it is important to predict liver cancer more explicitly by using machine learning. This study examines the survival prediction of a dataset of HCC based on three strategies. Originally, missing values are estimated using mean, mode, and k-Nearest Neighbor (k-NN). We then compare the different select features using the wrapper and embedded methods. The embedded method employs Least Absolute Shrinkage and Selection Operator (LASSO) and ridge regression in conjunction with Logistic Regression (LR). In the wrapper method, gradient boosting and random forests eliminate features recursively. Classification algorithms for predicting results include k-NN, Random Forest (RF), and Logistic Regression. The experimental results indicate that Recursive Feature Elimination with Gradient Boosting (RFE-GB) produces better results, with a 96.66% accuracy rate and a 95.66% F1-score.

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“…The literature review of the existing methods of cancer diagnosis is stated. Irfan Ullah Khan et al [ 7 ] developed a model for the early analysis of cervical cancer with the aid of a reduced risk set of features and three ensemble classification strategies, such as Random Forest (RF), extreme Gradient Boosting (XGBoost), and AdaBoost, along with an FF algorithm for a hyperparameter tuning process. The model achieved a poor sensitivity measure, which is considered the method’s major drawback.…”

Section: Literature Surveymentioning

confidence: 99%

Cancer Diagnosis of Microscopic Biopsy Images Using a Social Spider Optimisation-Tuned Neural Network

Balaji

Chidambaram

2021

Diagnostics

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One of the most dangerous diseases that threaten people is cancer. If diagnosed in earlier stages, cancer, with its life-threatening consequences, has the possibility of eradication. In addition, accuracy in prediction plays a significant role. Hence, developing a reliable model that contributes much towards the medical community in the early diagnosis of biopsy images with perfect accuracy comes to the forefront. This article aims to develop better predictive models using multivariate data and high-resolution diagnostic tools in clinical cancer research. This paper proposes the social spider optimisation (SSO) algorithm-tuned neural network to classify microscopic biopsy images of cancer. The significance of the proposed model relies on the effective tuning of the weights of the neural network classifier by the SSO algorithm. The performance of the proposed strategy is analysed with performance metrics such as accuracy, sensitivity, specificity, and MCC measures, and the attained results are 95.9181%, 94.2515%, 97.125%, and 97.68%, respectively, which shows the effectiveness of the proposed method for cancer disease diagnosis.

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Cervical Cancer Diagnosis Model Using Extreme Gradient Boosting and Bioinspired Firefly Optimization

Cited by 13 publications

References 21 publications

Automated Detection and Classification of Cervical Cancer Using Pap Smear Microscopic Images: A Comprehensive Review and Future Perspectives

Automated Detection and Classification of Cervical Cancer Using Pap Smear Microscopic Images: A Comprehensive Review and Future Perspectives

A Novel Method for Survival Prediction of Hepatocellular Carcinoma Using Feature-Selection Techniques

Cancer Diagnosis of Microscopic Biopsy Images Using a Social Spider Optimisation-Tuned Neural Network

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